1. Field of the Invention
The present invention relates to a pattern recognition device, a pattern recognition method and a computer program product.
2. Description of the Related Art
When performing speech recognition, a feature of speech to be recognized is compared with models that have learned speech features to determine which model is close to the speech to be recognized. In speaker-independent speech recognition, speaker and noise environment are different between when a model learns and when the model is used for speech recognition, so that bias occurs between the model and an input speech feature. As a representative method for reducing an influence exerted by the bias, there is a CMN method (see B. Atal, “Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification,” J. Acoust. Soc. AM., vol. 55, pp. 1304-1312, 1974).
In the CMN method, feature amounts in a certain defined time section are averaged and an average value thus obtained is subtracted from each feature amount, so as to remove the influence by the bias. The CMN method is effective as the method of reducing the influence by the bias, and a calculation amount thereof is small.
As another method of removing the effect of the bias, there is an MLLR method (see C. J. Leggetter and P. C. Woodland, “Maximum likelihood linear regression for speaker adaptation of continuous-density hidden Markov models”, Computer Speech and Language, vol. 9, pp. 171-185, 1995) and an SBR method (see U.S. Pat. No. 5,590,242). In the MLLR method, a condition in which the bias does not change in time is assumed. The SBR method uses a Hidden Markov Model (HMM) represented by a Gaussian distribution as the model. In the SBR method, the bias is corrected by approximating variance of the Gaussian distribution by a unit matrix in a bias section calculator of the MLLR method.
In the SBR method, an average of differences between an average vector of each model and each feature vector is used as a correction vector, and a feature vector is corrected by subtracting the average vector from the feature vector. Thus, the correction taking into account the influence of the noise and the like can be performed.
However, in the SBR method, because the variance of the model distribution is approximated, there may be a case in which bias correction performance is deteriorated. On the other hand, in the MLLR method, because the correction vector is a weighted average obtained by weighting the difference between the average vector of the models and the feature vector with standard deviation, and a condition in which the bias does not change in time is assumed, the correction performance is deteriorated when the bias sequentially changes.